Relationship between Fatigue and Cytokine Levels in Patients Age 50+ with Acute Myeloid Leukemia (AML).
Bibliographic record
Abstract
Abstract Several studies have found fatigue to be the most common, persistent, and disabling symptom affecting patients with AML. Effective measures to prevent or treat fatigue have yet to be found. Cytokines, biological markers of inflammation, may represent one potential intervention target, but data on the fatigue-cytokine relationship in AML are limited to one small published study. We examined this relationship in patients age 50 or older with AML, fluent in English, within one year of AML diagnosis, and free of any other active malignancy. Fatigue was measured using the Functional Assessment of Cancer Therapy (FACT) Fatigue subscale and a single-item global fatigue scale. Quality of life (QOL) and depression were assessed using the European Organization for the Research and Treatment of Cancer (EORTC) core questionnaire (QLQ-C30) and the Hospital Anxiety and Depression Scale (HADS), respectively. Blood was simultaneously drawn for quantitative measurement of a panel of cytokines. Patients were reassessed 4–6 weeks later, and the same set of questionnaires were administered and a second blood sample drawn. Pearson’s correlation was used to examine relationships between individual cytokines and fatigue scores. Variables were transformed as necessary. For patients with data at two time points, changes in fatigue scores were correlated with changes in cytokines. At the time of submission, 31 patients (20 males; 11 females) have been enrolled (mean age 67 y; range 52–84). 32% had not started active chemotherapy or were receiving best supportive care, while the remainder were undergoing active chemotherapy. At the first time point (t1), no moderate or strong correlations were observed with any of the cytokines and at least one fatigue score. Weak correlations (0.30 Based on these data, the most consistent relationships between concentrations of cytokines and fatigue were noted with IL-5, IL-6, and IL-8, and these show the most promise for future studies. Our study bore two notable limitations: the first was the small sample size, and the second was the recruitment of patients at differing time points during their treatment course. Further patient enrolment and data collection are underway to validate our findings and clarify the significance of these preliminary results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".